CN116051849B - Brain network data feature extraction method and device - Google Patents
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Abstract
The invention discloses a brain network data feature extraction method and device, wherein the method is based on Embedding (Embedding) and Grassman manifold (Grassmannian Manifold) to obtain multi-layer brain network node information low-dimensional representation, the multi-layer brain network node information is subjected to low-dimensional representation by using an Embedding method, the Embedding representation is optimized based on the Grassman manifold to obtain more representative and meaningful low-dimensional node feature information, and a low-dimensional brain function connection network can be obtained by reconstructing the low-dimensional representation. According to the invention, by carrying out dimension reduction and key extraction on complex multi-layer brain network information, more representative and more effective multi-layer brain network node low-dimensional representation information is obtained, the utilization rate of multi-layer brain network data information is effectively improved, and brain researches such as disease diagnosis, node detection and the like with higher accuracy are realized by using less data volume.
Description
Technical Field
The invention relates to the field of medical image and network information processing, in particular to a brain network data feature extraction method and device.
Background
Magnetic resonance imaging (Magnetic Resonance Imaging, MRI) is a type of tomographic imaging generated by physical phenomena. The method utilizes the magnetic resonance phenomenon to obtain electromagnetic signals from a human body, and based on the principle of the magnetic resonance phenomenon, space encoding is carried out on the magnetic resonance signals to reconstruct human body images. The nuclear magnetic resonance imaging has multifunction, can not only perform standard structural contrast on soft tissues, but also reflect other tissue attributes and dynamic characteristics, and the brain nuclear magnetic resonance imaging can reflect tissue attributes and dynamic characteristics such as haemodynamics, water molecule diffusion and the like, can obtain brain MRI data with advanced contrast, and can provide substitute markers for physiological processes and markers of brain regions of human bodies. In brain functional magnetic resonance imaging, contrast in the data can represent blood volume, blood oxygen amount, difference between blood oxygen amount and relative blood oxygen level, and the like, and is a commonly used multi-layer brain network disease diagnosis technology.
The realization of automatic diagnosis of multi-layer brain network diseases is one of research hotspots in the related fields of computers, artificial intelligence, medical images and the like. The human brain network is intricate and complex, has huge data information, and is a key for improving the diagnosis accuracy rate by deep research on nuclear magnetic resonance imaging images, mining useful information contained in the nuclear magnetic resonance imaging images, obtaining multi-layer brain network information and applying the characteristics to disease diagnosis research. The network embedding method (Network Embedding) is an effective network characterization learning method, and aims to represent a high-dimensional and sparse vector space by using a low-dimensional and dense vector space. A series of studies have shown that using network embedding can effectively find a mapping function, dimension-reduce complex brain network data information, and convert each node in the network into a potential representation of low dimension.
The multi-layer brain MRI data are processed, the multi-layer brain network function connection network is obtained, the functional network is used for researching diseases such as Alzheimer's disease and the like, and the multi-layer brain MRI data play a positive role in diagnosing diseases, developing diseases and the like. In recent years, some related technical means for researching multi-layer brain network data appear, and students propose to research the multi-layer brain network data by using a dimension reduction mode to improve the effectiveness of information, but the existing technical means only research single-layer information of the multi-layer brain network, generally use common modes such as feature extraction and the like to carry out information mining, have a single mode, and are difficult to comprehensively acquire all key information of the multi-layer brain network. And only the information in the layers is researched, the information of the multi-layer brain network cannot be comprehensively utilized, and part of important interaction relations are lost. The difficulty in obtaining comprehensive data for continued research results in certain drawbacks and deficiencies in related research and automated diagnosis of multi-layer brain network diseases.
Disclosure of Invention
Aiming at the defects of the prior art, the invention constructs multi-layer brain network data comprising information in the same-frequency-band layer and information among different-frequency-band interlayers of brain nodes, fully obtains effective information of the multi-layer brain network data, processes the information in the fusion layer and the interlayer information, reduces the dimension of the complex and huge multi-layer brain network data, and obtains embedded representation vectors of the different brain nodes after embedded learning and iterative optimization on manifold, namely, the characteristic representation vectors of the brain nodes. The invention provides a new multi-layer brain network embedded frame, which is characterized in that the connection dependence and the cross-layer network dependence in a brain region node layer are simultaneously modeled in a unified optimization frame to obtain an embedded learning representation joint frame, and the joint representation function is used as a loss function to optimize on a manifold to obtain the embedded vector public representation of the multi-layer brain network node, namely the extracted brain network data characteristics.
The technical scheme adopted by the invention is as follows:
the brain network data characteristic extraction method specifically comprises the following steps:
constructing an intra-layer node embedded representation, an inter-layer node embedded representation and an intra-layer network consistency embedded representation based on the multi-layer brain function connection network; combining the intra-layer node embedded representation, the inter-layer node embedded representation and the intra-layer network consistency embedded representation, and adding an adaptive weight item alpha to each layer in the intra-layer network consistency embedded representation i I is an index of layers in the multi-layer brain function connection network, resulting in a joint embedded representation framework;
and optimizing a joint embedded representation framework on the Grassman manifold, wherein the optimized embedded representation F of the intra-layer and inter-layer network consistency is the extracted low-dimensional brain network data characteristic.
Further, the joint embedded representation framework is specifically:
where n is the number of layers in the multi-layer brain function connection network, F i Is the i-th layer-to-layer interconnection in the multi-layer brain function connection networkEmbedding a representation vector into an intra-layer node corresponding to the matrix, L i Is Laplacian matrix corresponding to the i-th layer-by-layer connection matrix in the multi-layer brain function connection network, alpha D Represents the interlayer weight parameter, W ij Is an interlayer connection matrix of an ith layer and a jth layer in a multi-layer brain function connection network, K ij Representing the interaction matrix between the ith and jth layers, K ij Approximately equal to F i T D ij F j ；D ij Is the i/j-th layer interlayer connection matrix W ij Corresponding diagonal matrix, alpha i Is self-adaptive weight, F is intra-layer and inter-layer network consistency embedded representation;
further, the optimization joint embedding representation framework on the glasman manifold is specifically:
by using an exponential mapping operation
And->
Mapping from tangent space to Grassman manifold, embedding F in a representation frame for union i Carrying out iterative optimization with F; wherein->
And->
Respectively, a Grassman gradient of the joint embedded representation framework on manifold space;
weight alpha by constructing an adaptive optimized loss function i Performing iterative optimization, wherein the loss function is expressed as follows:
wherein lambda, gamma D Representing the weight parameters.
Further, the Grassman gradient of the manifold spatial joint embedding representation framework is obtained by the following method:
respectively corresponding F according to joint embedded representation framework i And F, obtaining F i And F Euclidean gradient based on joint embedding representation framework
And->
；
Euclidean gradient is projected onto tangent space by orthogonal projection, and Grassman gradient can be obtained
And (3) with
。
Further, the multi-layer brain function connection network is constructed and obtained by the following method:
acquiring brain MRI data of a patient, and calculating correlations between brain nodes of different brain regions, wherein the correlations between brain nodes in the same frequency band are used as intra-layer connection networks of the brain nodes; the brain region node interrelation between different frequency bands is used as an interlayer connection network of brain region nodes, and the interlayer connection network form a multi-layer brain function connection network.
The brain network data feature extraction device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the brain network data feature extraction method when executing the computer program.
A storage medium containing computer executable instructions which when executed by a computer processor implement the aforementioned brain network data feature extraction method.
A brain network data feature extraction device, comprising:
the multi-layer brain network embedded representation joint framework module is used for simultaneously modeling intra-layer connection and cross-layer network dependence in a unified optimization framework, obtaining embedded representation learning, constructing an intra-layer embedded representation model and an inter-layer embedded representation model construction algorithm of the multi-layer brain network, referencing the concept that most of human brain region node connection has network consistency, adding multi-layer brain network consistency constraint items, and obtaining a multi-layer brain function connection network construction joint embedded representation framework:
where n is the number of layers in the multi-layer brain function connection network, F i Is the embedded expression vector of the intra-layer node corresponding to the i-th layer-to-layer connection matrix in the multi-layer brain function connection network, L i Is Laplacian matrix corresponding to the i-th layer-by-layer connection matrix in the multi-layer brain function connection network, alpha D Represents the interlayer weight parameter, W ij Is an interlayer connection matrix of an ith layer and a jth layer in a multi-layer brain function connection network, K ij Representing the interaction matrix between the ith and jth layers, K ij Approximately equal to F i T D ij F j ；D ij Is the i/j-th layer interlayer connection matrix W ij Corresponding diagonal matrix, alpha i Is self-adaptive weight, F is intra-layer and inter-layer network consistency embedded representation;
the optimization module optimizes the joint embedded representation framework on the Grassman manifold, and the embedded representation F of the network consistency between the layers obtained by optimization is the extracted brain network data characteristic; specifically, optimizing a loss function on a Grassman manifold, describing the spatial distribution consistency of different embedded vectors through the Grassman manifold distance, and obtaining the optimal multi-layer brain network node low-dimensional representation information through continuous optimization on the manifold; introducing self-adaptive multi-layer brain network weight information in the optimization process, carrying out self-adaptive weight adjustment on the multi-layer brain network in the optimization process, and adding a non-negative weight alpha in order to combine all embedded information and learn complementary attributes of different networks i To adjust the embedded learning, alpha i The larger i is the more important the role view in learning to obtain low-dimensional embedding and vice versa, the better and important network information proceeding right is obtained in an adaptive mannerThe optimization accuracy and the effectiveness of the node vector representation are improved.
Further, the multi-layer brain network node information processing system further comprises a nuclear magnetic resonance imaging data processing module, wherein the multi-layer brain network data in different frequency bands are respectively processed by processing MRI data, multi-layer brain network function connections in the same frequency band and multi-layer brain network function connections between different frequency bands are respectively obtained, the multi-layer brain network is processed, the functional connection multi-layer brain network formed by effective data is obtained, and multi-layer brain network function node information is constructed.
A brain disease prediction system, comprising:
the brain network data feature extraction device is used for extracting and obtaining brain network data features;
and the brain disease prediction module is used for predicting the brain disease based on the characteristics obtained by the brain network data.
Further, the brain disease prediction module is a neural network or a classification decision tree.
The beneficial effects of the invention are as follows: the multi-layer brain network information is calculated by comprehensively applying the node information in different frequency bands and the node information between the frequency bands of the multi-layer brain network, so that the problem that the multi-layer brain network data is not fully applied is solved; the network embedded computation is applied to the multi-layer brain network information processing, so that the multi-layer brain network data with high dimensionality and high complexity is effectively converted into the low-dimensional potential representation of the multi-layer brain network nodes, the effectiveness of the multi-layer brain network node information is improved, and key areas of the multi-layer brain network are better divided and positioned; the concept of multi-layer brain network consistency and self-adaptive weight is introduced, the low-dimensional multi-layer brain network node expression vector is accurately and comprehensively obtained, namely the extracted brain network data characteristic is obtained, more useful information is provided for multi-layer brain network disease diagnosis, and the effectiveness of disease research and diagnosis is further improved.
Drawings
FIG. 1 is a flow chart of a method for extracting brain network data characteristics according to the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the present invention;
fig. 3 is a processed multi-layer brain network data display diagram, wherein (a) in fig. 3 is a multi-layer brain network matrix result diagram, (b) in fig. 3 is a multi-layer brain network stereoscopic display diagram, and (c) in fig. 3 is a multi-layer brain network top display diagram;
FIG. 4 is a graph of clustering results of extracted brain network data features according to an embodiment of the present invention;
fig. 5 is a comparison diagram of a multi-layer brain network obtained after the reconstruction of the brain network data features obtained by the invention and a multi-layer brain network constructed by a real method and other methods, wherein (a) in fig. 5 is a simulated brain network data visual display diagram used for calculation, (b) in fig. 5 is a brain area node network visual display diagram obtained by using the embedded vector reconstruction of the invention, (c) in fig. 5 is a brain area node network visual display diagram obtained by using the Muti-Layerd Network Embedding algorithm, and (d) in fig. 5 is a brain area node network visual display diagram obtained by using the NMF algorithm;
FIG. 6 is a diagram of a brain network data feature extraction device according to a second embodiment of the present invention;
fig. 7 is a diagram of an electronic device according to a second embodiment of the present invention;
fig. 8 is a block diagram of a brain disease prediction system according to a third embodiment of the present invention.
Detailed Description
The method constructs the multi-layer brain network by interconnecting the node areas between the multi-layer brain network data and different frequency bands in the same frequency band. The multi-layer brain network data is subjected to dimension reduction, so that low-dimension effective node expression vectors are extracted brain network data characteristics, the data volume of subsequent calculation and research is reduced, the calculation efficiency is improved, and more effective information is provided for multi-layer brain network disease research and diagnosis. The invention will be further described with reference to specific examples and figures.
Example 1
FIG. 1 is a flow chart of the method of the present invention, wherein the method of the present invention comprises the steps of constructing multi-layer brain network data, constructing an inter-layer joint optimization framework, introducing network consistency and self-adaptive weight parameters, and optimizing embedded representation on manifold to obtain low-dimensional node vector representation, namely extracting and obtaining the low-dimensional characteristics of the brain network data; as shown in the flow chart of the embodiment of fig. 2, the method specifically includes the following steps:
step one: constructing a multi-layer brain function connection network;
in general, the multi-layer brain function connection network is constructed and obtained based on brain medical image data such as MRI of a patient, and the multi-layer brain function connection network is obtained by acquiring brain MRI data of the patient, processing the data, and calculating multi-layer brain network data of different frequency bands; the multi-layer brain function connection network W is formed by interrelation between nodes of different brain regions of the brain, and comprises an intra-layer connection network and an inter-layer connection network.
In this embodiment, taking an example of an n=10, n=3 simulated multi-layer brain function connection network, a multi-layer brain function connection network with 10 brain area nodes and 3 layers is constructed.
The simulation data is based on MRI data to perform network construction, an intra-layer connecting network and an inter-layer connecting network are generated through simulation, the mutual relations of brain area nodes in N same frequency bands are simulated to be used as the intra-layer connecting network of brain area nodes, and an intra-layer connecting matrix W is constructed through the network data of the same frequency band i The method comprises the steps of placing the brain region node interrelationships among different frequency bands in a diagonal line part of multi-layer brain network simulation data adjacency matrix visualization as a brain region node interlayer connection network, and constructing an interlayer connection matrix W by network data among layers, namely among different frequency bands ij The structure, wherein the subscript i, j is the index of the layer, i is not equal to j, i, j is not equal to (1,.. The n) and is placed at the corresponding positions of the upper triangle and the lower triangle of the multi-layer brain network simulation data adjacent matrix visualization, fig. 3 shows a multi-layer brain function connection network data adjacent matrix visualization diagram of the embodiment, three matrix modules at the diagonal part of the matrix shown in fig. 3 (a) are intra-layer connection networks, and the inter-layer connection of the matrix shown in fig. 3 (c) is corresponding to the inter-layer connection of the matrix, which represents the inter-connection representation of brain areas with the same frequency band; the six matrix modules other than the diagonal matrix shown in (a) of fig. 3 represent related connections of brain regions between different frequency bands, corresponding to the cross-layer connection information shown in (b) of fig. 3.
Step two: constructing a joint embedded representation framework for a multi-layer brain network, which specifically comprises the following steps:
(2.1) intra-layer node embedded representation: using the intra-layer connection matrix in the multi-layer brain function connection network obtained in the step one to construct an intra-layer node embedded representation, firstly using the formula: l (L) i =D i -W i Computing an intra-layer network laplace matrix, where D i Is an ith layer-by-layer interconnection matrix W i Corresponding diagonal matrix, each diagonal element being equal to W i The total connectivity sum of all nodes in the network; by F i Represents W i To embed vectors at W i To find the node low-dimensional embedded vector representation capable of keeping the original topological structure, for the intra-layer connection matrix of the ith layer, if two nodes are connected, the embedded vector representation between the two nodes can be forced to be similar, so that the intra-layer embedded representation can be expressed as an embedded vector and a Laplace matrix L i Is a trace of (1):
and obtaining an intra-layer mathematical optimization expression through optimizing the embedded representation, wherein the intra-layer mathematical optimization expression is as follows: />
。
(2.2) inter-layer node embedded representation: constructing an interlayer node embedded representation by using an interlayer connection matrix in the obtained brain network data W, and firstly using
Representing the interaction matrix between the i-th layer embedding and the j-th layer embedding, superscript w i 、w j Respectively representing the sizes of the connection matrixes in the ith layer and the jth layer, meeting the embedding condition, and finding K ij Approximately equal to->
Wherein D is ij Is the i/j-th layer interlayer connection matrix W ij A corresponding diagonal matrix, each diagonal element being equal to the interlayer connection matrix W ij The sum of the total connectivity of all nodes in the network. In embedded formIn the above, the interaction of the inter-layer embedded vector indicates that the inter-layer embedded vector should be connected with the inter-layer connection matrix W ij Same, solve for W ij The difference between the F-norm distance from the embedded expression of the i, j-th layer is calculated by minimizing W ij The difference between the node similarity embedded mathematical optimization expression and the embedding expression matrix between the i layer and the j layer is obtained to obtain the optimal embedding expression, and the node similarity embedded mathematical optimization expression between the i layer and the j layer can be obtained as->
；||*|| F 2 Representing the F-norm.
(2.3) intra-layer inter-layer network consistency embedding representation: constructing node embedded information representation F common between layers in a layer, wherein the node embedded information representation F is an orthogonal matrix and has the characteristics distributed on a Grassman manifold, and defining the embedded information representation F and the interlayer embedded information F i Distances distributed over the manifold, a consistency of the multi-layer embedded vector with the intra-layer embedded vector is calculated. Solving the direct trace tr of the embedded vectors to obtain the distance between the embedded vectors, maintaining the consistency of the optimization framework, and expressing the minimized manifold distance auxiliary item as:
ρ is a constant, typically the dimension of F can be taken, and the uniformity of the embedded vector distribution is continuously optimized by iteration;
(2.4) a multi-layer brain network node information federation framework: combining the intra-layer node embedded representation, the weight inter-layer node embedded representation and the intra-layer network consistency embedded representation, and adding an adaptive weight item alpha into a network consistency loss item i By adapting F i And (3) adjusting the proportion of the consistency of the network by the distance weight between the joint embedded representation frame and F to obtain the joint embedded representation frame:
wherein alpha is D Representing the interlayer weight parameter;
step three: optimizing the joint embedded representation framework on the Grassman manifold specifically comprises the following steps:
(3.1) computing a Grassman gradient of the joint embedding representation framework over the manifold space
And->
: first, according to joint embedded representation framework, F is respectively matched i And F, performing bias derivation to obtain Euclidean gradient of the two based on joint embedded representation framework
And->
The method comprises the following steps of:
euclidean gradient is projected onto tangent space by orthogonal projection, and Grassman gradient can be obtained
And (3) with
：
Wherein I is N*N Is an N x N identity matrix of size, N being the number of nodes.
(3.2) Using the novel Grassman gradient obtained in (3.1)
And->
By using an exponential mapping operation +.>
And->
Mapping from tangent space to Grassman manifold to obtain new F i And F.
(3.3) constructing an adaptive optimization loss function: by calculating F and F i Weight parameters are obtained by consistency of (1) and the ith F is adaptively adjusted i The specific weight occupied in the consistency calculation is calculated by introducing a constraint term with lambda
Computing alpha by Lagrangian multiplier i Obtaining a Lagrangian function as alpha i The loss function of (2) is as follows:
wherein, gamma D Representing the weight parameters;
(3.4) calculating and weighting alpha i Is iteratively optimized: by calculating Lagrangian function for alpha i Solution with lambda being 0 after derivation can be obtained
. And iterating the calculation process to obtain the optimal result. Wherein gamma is α Representing the weight parameters.
Step four: using adaptive weights and optimized F i And obtaining a multi-layer brain network node low-dimensional embedded information vector, namely embedding the intra-layer and inter-layer network consistency into a public representation F, namely extracting to obtain brain network data characteristics.
Fig. 4 is a graph of clustering results of an embedded representation obtained by processing simulation data (shown in fig. 3) by using the method of the present invention, in this embodiment, verification is performed by using a K clustering algorithm, it can be seen that in an embedded space, all multi-layer brain network nodes can be clustered into 3 types by the clustering algorithm, characteristics of the multi-layer brain function connection network and key nodes in the corresponding fig. 3 in the data are respectively met, embedded coordinates of the key nodes are in the middle of three areas, and meanwhile, the key nodes have similarity with other two multi-layer brain network modules, nine experiments are performed, and it can be found that nine detections all meet the characteristics of the data. The node information vector after dimension reduction is extracted to obtain the node key information of the brain network data characteristics which can well keep the original network, so that the data quantity is reduced, the node key information outside the inner layer is respectively kept, and the dimension reduction provides more efficient and accurate information for multi-layer brain network data research.
As shown in fig. 5, the correlation between brain area nodes is calculated by using the obtained embedded vector F, according to the correlation reconstruction network, (a) in fig. 5 is a simulation brain network data visualization display diagram for calculation, and (b) in fig. 5 is a brain area node network visualization display diagram obtained by using the embedded vector reconstruction of the present invention, and (C) in fig. 5 and (d) in fig. 5 are respectively comparison algorithms Muti-Layerd Network Embedding and NMF (Li J, chen C, to H, et al, multi-layered network embedding [ C ]// Proceedings of the 2018 SIAM International Conference on Data Mining, society for Industrial and Applied Mathematics, 688:684-692) to obtain a brain network data visualization diagram for embedding vector reconstruction, through which it can be seen that the reconstructed image of the present invention well reflects the modularity and connection characteristics of the original data, and the feature information of the original brain area node is better retained by the obtained embedded vector after dimension reduction.
Example two
The invention also provides an embodiment of a brain network data feature extraction device corresponding to the embodiment of the brain network data feature extraction method.
Referring to fig. 6, a brain network data feature extraction device provided by an embodiment of the present invention includes:
the multi-layer brain network embedded representation joint framework module constructs a joint embedded representation framework for the multi-layer brain function connection network;
and the optimization extraction module optimizes the joint embedded representation framework on the Grassman manifold, and the embedded representation F of the intra-layer-inter-layer network consistency obtained by optimization is the extracted brain network data characteristic.
The embodiment of the brain network data characteristic extraction device can be applied to any device with data processing capability, such as a computer or the like.
The apparatus embodiments may be implemented in software, or in hardware or a combination of hardware and software. Taking a software implementation as an example, as a device in a logic sense, a processor of any device with data processing capability reads corresponding computer program instructions in a nonvolatile memory to a memory to run, and as shown in fig. 7, the device is a hardware structure diagram of any device with data processing capability where the brain network data feature extraction device of the present invention is located, except that the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 7, where any device with data processing capability in an embodiment generally includes other hardware according to an actual function of the any device with data processing capability, which is not described herein again.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
The embodiment of the invention also provides a computer readable storage medium, on which a program is stored, which when executed by a processor, implements a brain network data feature extraction method in the above embodiment.
The computer readable storage medium may be an internal storage unit, such as a hard disk or a memory, of any of the data processing enabled devices described in any of the previous embodiments. The computer readable storage medium may be any device having data processing capability, for example, a plug-in hard disk, a Smart Media Card (SMC), an SD Card, a Flash memory Card (Flash Card), or the like, which are provided on the device. Further, the computer readable storage medium may include both internal storage units and external storage devices of any data processing device. The computer readable storage medium is used for storing the computer program and other programs and data required by the arbitrary data processing apparatus, and may also be used for temporarily storing data that has been output or is to be output.
Example III
Corresponding to the embodiment of the method for extracting the brain network data characteristics, the invention also provides an embodiment of a brain disease prediction system based on the brain network data characteristics obtained by the extraction of the method/device.
As shown in fig. 8, the brain disease prediction device comprises a brain network data feature extraction device of the invention for extracting and obtaining brain network data features;
and the brain disease prediction module is used for predicting the brain disease based on the characteristics obtained by the brain network data. The brain disease prediction module can be constructed and obtained based on a conventional method by adopting a neural network, a classification decision tree and other structures commonly used in the prior art.
In this embodiment, brain network data feature extraction is performed on MRI data of 124 patients, and the resulting embedded low-dimensional feature vector is used to detect and verify compulsive disorder brain disease. The obtained embedded vector is subjected to double-sample test to obtain a significance index p_value= 0.001599, so that the brain network data characteristics extracted by the method have better significance in the aspect of disease diagnosis;
disease classification was performed on embedded low-dimensional feature vectors obtained from patient MRI data, and the results are shown in table 1. The interlayer weight parameters used in the calculation are adjusted, the effectiveness of interlayer information is verified, the SVM classification is carried out by using the embedding vector F obtained by the calculation, and the effect of improving the disease diagnosis is good by introducing the interlayer information.
TABLE 1 relationship between interlayer coefficient and accuracy
| α D | Precision Accurary |
| 0 | 0.6958 |
| 0.1 | 0.8994 |
| 0.5 | 0.8578 |
| 2 | 0.8125 |
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary or exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (8)
1. The brain network data characteristic extraction method is characterized by comprising the following steps of:
constructing an intra-layer node embedded representation, an inter-layer node embedded representation and an intra-layer network consistency embedded representation based on the multi-layer brain function connection network; combining the intra-layer node embedded representation, the inter-layer node embedded representation and the intra-layer network consistency embedded representation, and forming a first intra-layer networkAdding an adaptive weight term alpha to each layer in an adaptive embedded representation i I is an index of layers in the multi-layer brain function connection network, resulting in a joint embedded representation framework;
optimizing a joint embedding representation framework on a Grassman manifold, and embedding representation F for the network consistency between layers obtained by optimization, namely, the extracted low-dimensional brain network data characteristics;
the joint embedded representation framework is specifically:
where n is the number of layers in the multi-layer brain function connection network, F i Is the embedded expression vector of the intra-layer node corresponding to the i-th layer-to-layer connection matrix in the multi-layer brain function connection network, L i Is Laplacian matrix corresponding to the i-th layer-by-layer connection matrix in the multi-layer brain function connection network, alpha D Represents the interlayer weight parameter, W ij Is an interlayer connection matrix of an ith layer and a jth layer in a multi-layer brain function connection network, K ij Representing the interaction matrix between the ith and jth layers, K ij Equal to F i T D ij F j ；D ij Is the i/j-th layer interlayer connection matrix W ij Corresponding diagonal matrix, alpha i Is self-adaptive weight, F is intra-layer and inter-layer network consistency embedded representation;
the optimization joint embedding representation framework on the Grassman manifold is specifically as follows:
by using an exponential mapping operation
And->
Mapping from tangent space to Grassman manifold, embedding F in a representation frame for union i Carrying out iterative optimization with F; wherein->
And->
Respectively, a Grassman gradient of the joint embedded representation framework on manifold space;
weight alpha by constructing an adaptive optimized loss function i Performing iterative optimization, wherein the loss function is expressed as follows:
wherein lambda, gamma D Representing the weight parameters.
2. The method according to claim 1, characterized in that the goldman gradient of the manifold spatially joint embedding representation framework is obtained by:
respectively corresponding F according to joint embedded representation framework i And F, obtaining F i And F Euclidean gradient based on joint embedding representation framework
And->
；
Euclidean gradient is projected onto tangent space through orthogonal projection to obtain Grassman gradient
And->
。
3. The method according to claim 1, wherein the multi-layered brain function connection network is constructed by:
acquiring brain MRI data of a patient, and calculating correlations between brain nodes of different brain regions, wherein the correlations between brain nodes in the same frequency band are used as intra-layer connection networks of the brain nodes; the brain region node interrelation between different frequency bands is used as an interlayer connection network of brain region nodes, and the interlayer connection network form a multi-layer brain function connection network.
4. A brain network data feature extraction device, comprising:
the multi-layer brain network embedded representation joint framework module constructs intra-layer node embedded representation, inter-layer node embedded representation and intra-layer network consistency embedded representation based on the multi-layer brain function connection network; combining the intra-layer node embedded representation, the inter-layer node embedded representation and the intra-layer network consistency embedded representation, and adding an adaptive weight item alpha to each layer in the intra-layer network consistency embedded representation i I is an index of layers in the multi-layer brain function connection network, resulting in a joint embedded representation framework; the joint embedded representation framework is specifically:
where n is the number of layers in the multi-layer brain function connection network, F i Is the embedded expression vector of the intra-layer node corresponding to the i-th layer-to-layer connection matrix in the multi-layer brain function connection network, L i Is Laplacian matrix corresponding to the i-th layer-by-layer connection matrix in the multi-layer brain function connection network, alpha D Represents the interlayer weight parameter, W ij Is an interlayer connection matrix of an ith layer and a jth layer in a multi-layer brain function connection network, K ij Representing the interaction matrix between the ith and jth layers, K ij Equal to F i T D ij F j ；D ij Is the i/j-th layer interlayer connection matrix W ij Corresponding diagonal matrix, alpha i Is self-adaptive weight, F is intra-layer and inter-layer network consistency embedded representation;
the optimization joint embedding representation framework on the Grassman manifold is specifically as follows:
by using an exponential mapping operation
And->
Mapping from tangent space to Grassman manifold, embedding F in a representation frame for union i Carrying out iterative optimization with F; wherein->
And->
Respectively, a Grassman gradient of the joint embedded representation framework on manifold space;
weight alpha by constructing an adaptive optimized loss function i Performing iterative optimization, wherein the loss function is expressed as follows:
wherein lambda, gamma D Representing the weight parameters;
and the optimization extraction module optimizes the combined embedded representation framework on the Grassman manifold, and the embedded representation F with the optimized intra-layer-inter-layer network consistency is the extracted low-dimensional brain network data characteristic.
5. The apparatus of claim 4, further comprising a magnetic resonance imaging data processing module for acquiring MRI data of the brain of the patient, calculating correlations between different brain area nodes of the brain, wherein the brain area node correlations within the same frequency band are used as intra-layer connection networks of the brain area nodes; the brain region node interrelation between different frequency bands is used as an interlayer connection network of brain region nodes, and the interlayer connection network form a multi-layer brain function connection network.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program when executed by the processor implements the brain network data feature extraction method of any one of claims 1-3.
7. A storage medium containing computer executable instructions which when executed by a computer processor implement the brain network data feature extraction method of any one of claims 1-3.
8. A brain disease prediction system, comprising:
the brain network data feature extraction device according to claim 4 or 5, for extracting and obtaining brain network data features;
and the brain disease prediction module is used for predicting the brain disease based on the characteristics obtained by the brain network data.
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